max planck institut
informatik
informatik
| Lecturer | Thomas Lengauer |
| Teaching Assistant | Peter Ebert |
| Language | English |
| Lecture |
Wednesday, 10:00 - 12:00, Campus E2.1 (CBI building), room 007 First lecture will be held on Oct. 31, 2012 in E2.1, room 007 |
| Tutorial |
Wednesday, 12:00 - 14:00, E2.1 (CBI building), room 007 First tutorial on Nov. 14. |
| Office hours |
Thomas Lengauer: after each lecture Peter Ebert: By appointment, Campus E1.4 (MPII), Room 508 |
In order to successfully participate, you must register for the lecture in the LSF/HISPOS system of Saarland University. Additionally, please write an e-mail to the teaching assistant:
| Subject line: | [SL2] Registration |
| Body: | Last name, first name official e-mail address* Your major** |
*this means: mail account from Saarland University, the CBI, the MPI or similar
**e.g. bioinformatics, CS
In order to take the exam, register (i) in the LSF/HISPOS for the lecture and (ii) write an email to the TA.
| Subject line: | [SL2] Exam Registration |
| Body: | Last name, first name Matriculation number Your major Language (eng or ger) |
Lecture slides, tutorial handouts and problem sets are available in the password protected area.
The course will be the second part of a two semester course on Statistical Learning. The first part (SS 2012) concentrated on chapters 1-5 and 7-10 of the book The Elements of Statistical Learning, Springer (second edition, 2009). The second part will present the remaining bookchapters, focusing on advanced topics in supervised and unsupervised leaning, such as kernel methods, SVMs, neural networks, random forests and clustering. The theoretical models will be illustrated with interesting applications, out of which many are challenging problems in the field of bioinformatics. As in the previous semester, there will be two hours of lecture per week and one hour of tutorial (V2/Ü1), however, the tutorial will actually be two hours every other week.
This course covers a subject that is relevant for computer scientists in general as well as for other scientists involved in data analysis and modeling. It is not limited to the field of computational biology.
Both parts of this lecture fulfill the requirements for the curricula of computer science and bioinformatics as optional course with 5 credit points (Spezialvorlesung, 5 Leistungspunkte).
The course is targeted to advanced students in math, computer science and general science with mathematical background. Students should know linear algebra and have basic knowledge of statistics. Attendance of Statistical Learning I is recommended, however not required if a student has basic knowledge in machine learning
Theoretical assignments are handed out every other week and are due two weeks after. Additionally, there will be programming assignments, possibly in the form of several smaller projects. Theoretical problem sets will involve mathematical proofs as well as testing the understanding of methods presented in the lecture and their relations. The R statistical programming language is required for the programming assignments in which the methods presented in the lecture will be applied to real-world data.
You need a cumulative 50% points for each the theoretical problem sets and the programming assignments respectively to be admitted to the oral exam.
Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2009. The readers of the course are encouraged to acquire this book. You can download it as a PDF file from the dedicated page on Tibshirani's web site. More information on this book, as well as a contents listing can be found on the Springer web site.
Additional literature can be found in the library; the reserve list for the lecture can be found here: library reserve list for 'Elements of Statistical Learning II'
Please keep in mind that only the book by Hastie, Tibshirani and Friedman will be covered in the lecture.
The tutorials focus on the problem sets. A very brief reiteration of parts of the lecture is also given. Homework assignments will cover theoretical proofs and programming excercises with roughly equal weight.
The programming language that we use is R - a language for statistical computing. It is freely available for Windows and Linux and - as a vectorized programming language - is ideally suited for the problems we will encounter. There are also many freely available packages (or libraries) to perform a variety of classification and regression tasks, or to visualize the results of statistical analyses in a convenient way.